Bayesian Longitudinal Modeling of Early Stage Parkinson’s Disease Using DaTscan Images

Abstract

This paper proposes a disease progression model for early stage Parkinson’s Disease (PD) based on DaTscan images. The model has two novel aspects: first, the model is fully coupled across the two caudates and putamina. Second, the model uses a new constraint called model mirror symmetry (MMS). A full Bayesian analysis, with collapsed Gibbs sampling using conjugate priors, is used to obtain posterior samples of the model parameters. The model identifies PD progression subtypes and reveals novel fast modes of PD progression.